Our Dataset

Wages by Education

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This dataset details median wages categorized by education level, race, and gender from 1973 to 2022 in the United States. It allows for a detailed examination of how educational achievement intersects with racial backgrounds to impact wage outcomes over time. The dataset features information on year, education level (ranging from less than high school to advanced degrees), and median wages for White, Black, and Hispanic individuals.

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Analytical Perspective

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From a feminist perspective, the wage dataset underscores that women with lower educational levels are more likely to experience significant wage gaps, reflecting structural biases and gender discrimination in the labor market. Even among highly educated women, wage differentials persist. A graph showing wages by gender and education level over the years was created to highlight these persistent gender wage gaps.

 

Critical Race Theory (CRT) examines the intersection of race and education, highlighting how systemic racial discrimination impacts wage outcomes for people of color. A bar chart was created to visualize wage disparities by race and education level. The chart clearly shows that even with the same level of education, wage gaps persist between White individuals and people of color, with White individuals generally earning more.

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A Marxist analysis of the wage dataset reveals how education serves to perpetuate class structures within a capitalist society. By analyzing wage data segmented by educational attainment, it becomes evident that higher education correlates with higher wages, but these benefits are not equally accessible to all socioeconomic groups. The data shows that individuals from lower socioeconomic backgrounds face significant barriers to accessing higher education and, consequently, higher-paying jobs. This dynamic reinforces existing economic inequalities. To illustrate this, a series of graphs were created showing wages by education level over the years, emphasizing how economic mobility remains limited for those starting with fewer resources.

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Insights and Phenomena

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1. Economic Returns to Education: The dataset shows the link between higher education and increased wages, highlighting its influence on economic outcomes.

2. Racial Disparities: By segmenting data by race, the dataset can illuminate disparities in wage outcomes among different racial groups with similar educational attainments.

3. Gender Wage Gap: This dataset can be cross-referenced with gender wage data to explore how gender intersects with education and race to affect wage outcomes.

4. Trends Over Time: The dataset helps track changes in wage disparities and economic returns to education over several decades, highlighting progress or regression in economic equality.

5. Class Implications: The dataset shows how wage differences by education level highlight class structures and education's role in maintaining or challenging them.

 

Limitations

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1. Oversimplification: The dataset may oversimplify the relationship between education and wages by not accounting for variations within educational categories or other factors like job tenure and industry differences.

2. Geographic Disparities: It does not consider geographic variations that influence wage differentials, such as cost of living, economic conditions, and labor market demand in different regions.

3. Non-Wage Benefits: The dataset focuses primarily on wage data and may not capture non-wage benefits or broader economic indicators.

4. Intersectionality: While the dataset includes race and education level, it does not directly address the intersection of multiple identities, such as how gender and race together affect wage outcomes.

5. Temporal Changes: Economic conditions and labor market dynamics have changed significantly over the decades covered by the dataset. The dataset may not fully capture these temporal changes' impact on wages and employment opportunities.

 

Addressing Biases

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Recognizing these biases is important because it allows us to critically evaluate the dataset's implications. For example, if we assume that education is the primary driver of economic success, we might overlook other significant factors like access to quality education, economic background, or social networks.

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Conclusion

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In conclusion, while the dataset provides valuable insights into the relationship between education and economic outcomes, it is crucial to recognize its limitations and our own biases in interpreting the data. By adopting a more comprehensive approach to analyzing and addressing economic inequality, we can better understand and mitigate the complex factors that contribute to socioeconomic disparities.